Publications

OBJECTIVES: Invasive mechanical ventilation (IMV) is crucial for acute respiratory distress syndrome (ARDS) management, but mortality remains high. While spontaneous breathing is key to weaning, excessive respiratory effort may injure the lung and diaphragm. Most existing data on respiratory effort during IMV are based on brief periods of observation, potentially underestimating the burden of inappropriate efforts. This study aims to characterize the evolution of respiratory effort over time in ARDS patients and its relation to survival. We hypothesized that nonsurvivors would spend a greater proportion of time in the high-effort range during the active breathing phase compared with survivors. DESIGN, SETTING, AND PATIENTS: In this prospective cohort study, we continuously recorded airway pressure, flow, esophageal, and gastric pressures in ARDS patients on mechanical ventilation during 7 days after the onset of spontaneous breathing. We analyzed physiologic respiratory effort variables, focusing on the proportion of time spent within defined effort ranges, and compared these data between ICU survivors and nonsurvivors. Statistical analysis was conducted using variance weighted methods to account for variability in the number of respiratory cycles analyzed per patient. This study is registered at ClinicalTrials.gov under identifier NCT06490523. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 1,485,405 respiratory cycles were analyzed from 26 ARDS patients (19 survivors, seven nonsurvivors). Nonsurvivors spent significantly more time in high effort (12% vs. 3%; p = 0.006). In contrast, survivors spent more time in the moderate-effort range (50% vs. 5%; p < 0.001). The time spend with high dynamic transpulmonary driving pressure (> 25 cm H2O) was also significantly different between groups (32% survivors vs. 74% nonsurvivors; p = 0.001). CONCLUSIONS: Patients who die of ARDS are more likely to be exposed to high respiratory effort for prolonged periods of time compared with survivors.

JTD Keywords: Acute respiratory distress syndrome, Adult patients, Epidemiology, Esophageal, Esophageal pressure, Evolution, Lung injury, Mechanical ventilation, Mortality, Pressure support ventilation, Pulmonary, Respiratory effort, Transpulmonary pressure

One of the most challenging problems in intensive care is still the process of discontinuing mechanical ventilation, called weaning process. Both an unnecessary delay in the discontinuation process and a weaning trial that is undertaken too early are undesirable. In this study, we analyzed respiratory pattern variability using the respiratory volume signal of patients submitted to two different levels of pressure support ventilation (PSV), prior to withdrawal of the mechanical ventilation. In order to characterize the respiratory pattern, we analyzed the following time series: inspiratory time, expiratory time, breath duration, tidal volume, fractional inspiratory time, mean inspiratory flow and rapid shallow breathing. Several autoregressive modeling techniques were considered: autoregressive models (AR), autoregressive moving average models (ARMA), and autoregressive models with exogenous input (ARX). The following classification methods were used: logistic regression (LR), linear discriminant analysis (LDA) and support vector machines (SVM). 20 patients on weaning trials from mechanical ventilation were analyzed. The patients, submitted to two different levels of PSV, were classified as low PSV and high PSV. The variability of the respiratory patterns of these patients were analyzed. The most relevant parameters were extracted using the classifiers methods. The best results were obtained with the interquartile range and the final prediction errors of AR, ARMA and ARX models. An accuracy of 95% (93% sensitivity and 90% specificity) was obtained when the interquartile range of the expiratory time and the breath duration time series were used a LDA model. All classifiers showed a good compromise between sensitivity and specificity.

JTD Keywords: autoregressive moving average processes, feature extraction, medical signal processing, patient care, pneumodynamics, signal classification, support vector machines, time series, ARX, autoregressive modeling techniques, autoregressive models with exogenous input, autoregressive moving average model, breath duration time series, classification method, classifier method, discontinuing mechanical ventilation, expiratory time, feature extraction, final prediction errors, fractional inspiratory time, intensive care, interquartile range, linear discriminant analysis, logistic regression analysis, mean inspiratory flow, patient respiratory volume signal, pressure support level, pressure support ventilation, rapid shallow breathing, respiratory pattern variability characterization, support vector machines, tidal volume, weaning trial, Analytical models, Autoregressive processes, Biological system modeling, Estimation, Support vector machines, Time series analysis, Ventilation